MRI Texture Analysis for Differentiating Nonfunctional Pancreatic Neuroendocrine Neoplasms From Solid Pseudopapillary Neoplasms of the Pancreas

Abstract

To evaluate the value of texture analysis on preoperative magnetic resonance imaging (MRI) for identifying nonfunctional pancreatic neuroendocrine neoplasms (NF-PNENs) and solid pseudopapillary neoplasms (SPNs). This retrospective study included 119 patients who underwent MRI, including T2-weighted imaging with fat-suppression, diffusion-weighted imaging (DWI), apparent diffusion coefficient, precontrast T1-weighted imaging with fat-suppression (T1WI+fs), and dynamic contrast-enhanced (DCE)-T1WI+fs. Raw data analysis, principal component analysis, linear discriminant analysis, and nonlinear discriminant analysis (NDA) were used to classify NF-PNENs and SPNs. The results are reported as misclassification rates. The images were simultaneously evaluated by an experienced senior radiologist without knowledge of the pathological results. The misclassification rate of the radiologist was compared to the MaZda (texture analysis software) results. Neural network classifier testing was used for validation. In addition, 30 textures for each MRI sequence were investigated. The misclassification rate of NDA was lower than that of other analyses. In NDA, DWI obtained the lowest value of 7.92%, but there was no significant difference among the sequences. The misclassification rate of the radiologist (34.65%) was significantly higher than that of NDA for all sequences. The validation results were good in the arterial phase and delayed phase. In the training set, entropy and sum entropy were optimal texture features on DWI and precontrast T1WI+fs, while the mean and percentile seemed to be the more discriminative features on DCE-T1WI+fs. Texture analysis can sensitively distinguish between NF-PNENs and SPNs on MRI, and percentile and mean of DCE-T1WI+fs images were informative for differentiation of neoplasms.